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Scattered storage is a storage assignment strategy where single items are isolated and distributed all around the shelves of a warehouse. This way, the probability of always having some items per stock-keeping units close-by is increased, which is intended to reduce the unproductive walking time during order picking. Scattered storage is especially suited if each order line demands just a few items, so that it is mainly applied by business-to-consumer online retailers. This paper formulates a storage assignment problem supporting the scattered storage strategy. We provide and test suited solution procedures and investigate important managerial aspects, such as the frequency with which refilling the shelves should be executed. The online appendix is available at https://doi.org/10.1287/trsc.2017.0779 .

This study proposes a method for time-efficient order picking based on a structural stability assessment (SSA) when target boxes inside box-stacking storage (BSS) on multi-layer racks are removed. This method performs optimal order picking by generating a path to directly pick the target box without first picking the upper boxes in the BBS, if it is possible to pick the target box directly. The SSA algorithm generates images of the complement structure by removing the target box within BBS and uses them as input data for the CNN model to evaluate the stability of the structure. To create the CNN model, we generated a dataset using CoppeliaSim simulation, considering the size and shape of the overall structure of the BBS, the size and number of each box, and the number of target boxes. The accuracy of the generated CNN model was 95.1% on test data, while it achieved 97% accuracy when using real-world data. This validation process confirmed that the algorithm can be effectively applied to real BBS logistics environments to perform optimal order picking.

PurposeIncreasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).Design/methodology/approachThrough a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.FindingsWe show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.Originality/valueGiven that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

An MIAPP-NALT system is an order picking system in which cases are picked at multiple in-the-aisle pick positions (MIAPP) and storage and retrieval operations are performed by a narrow aisle lift truck (NALT). In this paper, the operation of such a system involving three classes of stock keeping units with random demands for storage and retrieval operations is modeled as an M/G/1 queue, where “customers” are storage and retrieval requests, the “server” is the NALT, and retrieval requests have non-preemptive priority over storage requests. Our goal is to explore a methodology and solution method to obtain the optimal layout design of a class-based MIAPP-NALT system with stochastic demands and priority service. To this end, an operation time model of the system is developed and the first two moments for the operation time are derived. To overcome the challenge in finding the desired optimal layout, a near-optimal layout obtained via a heuristic approach is obtained at first and is improved afterwards. Based on the optimal layout, some valuable queueing results demonstrate the benefit of using a priority-based discipline. Moreover, some useful insights regarding the selection of dedicated versus random storage policies are obtained.

Due to the convenience of online grocery apps and home delivery, online grocery shopping has become popular in recent years. Globally, consumer behavior has significantly changed the consumption and purchase patterns of online grocery shopping. This study aimed to develop an efficient model for online grocery order fulfillment that both reduces costs and increases supply chain efficiency and sustainability. This study first aimed to develop the current picking model by adopting real-world data from a store in Riyadh, Saudi Arabia. Subsequently, four proposed models were developed to improve the efficiency and sustainability of the online grocery order fulfillment process. The results show a significant improvement in all models over the current picking model. The percentage improvements in fulfillment time per product are as follows: single order picking—8.33%; batch order picking—6.78%; zone order picking—3.08%; and hybrid order picking—13.20%, which combines zone and batch order picking. Retailers and online grocery apps could adopt these models to increase efficiency and sustainability. Also, these models have great potential for future research and improvement by optimizing product placement, in addition to picking methods and picking routes, which are the focus of this study.

Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm.

PurposeThe Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.Design/methodology/approachDetails about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.FindingsThe resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.Originality/valueA solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.

This article aims to investigate the impact of allowable human energy expenditure (HEE) of order pickers on the throughput of workers in manual order zone picking systems MOP. The method used in this research is the Monte Carlo simulation, used while considering many human and job factors. The results showed that a worker’s gender and an item’s weight have little effect on the HEE. On the other hand, body weight, walking speed, distance travelled, and the targeted zone significantly impacted the HEE, rest allowance, and throughput. For example, male pickers at a weight of 75 kg can move up to speed to 1 m/s and pick up items weighing up to 5 kg without reaching the allowable HEE rate, equal to 4.3 kcal/min, and, thus, no rest is needed. Female pickers at a weight of 75 kg reach the allowable HEE rate, equal to 2.6 kcal/min, at a very low speed of approximately 0.1 m/s when picking up items up to 5 kg, and, thus, frequent rest is needed, which leads to low throughput. To increase the throughput of female pickers, they can be assigned to pick up lighter items. Utilising Monte Carlo simulation to evaluate the HEE in MOP while considering many factors.

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization.